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Previous studies have successfully used causal inference methods to estimate measures of public health impact in occupational epidemiology, relying primarily on estimation of structural nested failure time models.
To compare these guidelines, we use a Markov model for type 2 diabetes to estimate measures related to primary prevention including the probability of a coronary heart disease (CHD) or stroke event, number-needed-to-treat (NNT), and expected medication cost per event avoided.
As with any studies attempting to estimate measures with high uncertainty, our study has associated limitations.
However, they demonstrate how difficult it is to estimate measures for the quality of retrieval results under real life conditions.
We propose predictions P4 (which reduces to P1 for individuals with fully observed covariates) may be used to estimate measures of ideal model performance on all individuals.
This set of optimal codons was then used as input in CodonW to estimate measures of codon adaptation index (CAI) and codon bias index (CBI).
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DECA uses data about the sensed process that is available at a node to estimate measured values and sleeping periods.
In a sense, focus shifts from attempting to estimate measured variables from predictive coefficients derived from regression equations to qualitatively examining the relationship of the potential for misclassification given a set of putative covariates.
This study, using clinical measures and a simulation model, is able to estimate outcome measures for the diabetes program in one Division.
To estimate statistical measures for the entire population of interest, each subject's measures were weighted.
Multivariate extreme value theory approach is used to estimate these measures.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com